I direct the Robot Learning and Reasoning Lab (RL2). We aim to build general-purpose and adaptable "brains" for robots in home, factory, healthcare, and search & rescue missions alike. Our work focuses on endowing robots with both flexible high-level planning abilities ("what to do next") and robust low-level sensorimotor control ("how to do it"). The research draws equally from Robotics and Machine Learning, with the following themes:

• Robot Learning from Human Data: Despite recent successes in Internet-scale AI, Robot Learning remains data-deprived. We are broadly interested in enabling robots to learn from human data, in particilar, data captured with wearable devices such as smart glasses. My CoRL'24 keynote talk covers our vision on this topic.
• Long-horizon Reasoning with Generative Models: We develop methods that enable robots to solve complex, extended tasks. Our approach combines neuro-symbolic and generative models to chain simpler behaviors into novel solutions, while modeling high-dimensional distributions for human collaboration, dynamics prediction, and exploratory play behaviors that support reasoning over extended time horizons.
• Robot Learning Systems: Robotics is as much science as system building. We are committed to developing high-quality, open-source robot hardware and software systems to demonstrate and promote "full-stack" progress in learning-based robotics.